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Working Paper
A Hitchhiker’s Guide to Empirical Macro Models
Canova, Fabio; Ferroni, Filippo (2021-09-04)
This paper describes a package which uses MATLAB functions and routines to estimate VARs, local projections and other models with classical or Bayesian methods. The toolbox allows a researcher to conduct inference under various prior assumptions on the parameters, to produce point and density forecasts, to measure spillovers and to trace out the causal effect of shocks using a number of identification schemes. The toolbox is equipped to handle missing observations, mixed frequencies and time series with large cross-section information (e.g. panels of VAR and FAVAR). It also contains a number ...
Working Paper Series , Paper WP-2021-15

Working Paper
Simpler Bootstrap Estimation of the Asymptotic Variance of U-statistic Based Estimators
Hu, Luojia; Honore, Bo E. (2015-09-15)
The bootstrap is a popular and useful tool for estimating the asymptotic variance of complicated estimators. Ironically, the fact that the estimators are complicated can make the standard bootstrap computationally burdensome because it requires repeated re-calculation of the estimator. In Honor and Hu (2015), we propose a computationally simpler bootstrap procedure based on repeated re-calculation of one-dimensional estimators. The applicability of that approach is quite general. In this paper, we propose an alternative method which is specific to extremum estimators based on U-statistics. ...
Working Paper Series , Paper WP-2015-7

Working Paper
Selecting Primal Innovations in DSGE models
Ferroni, Filippo; León-Ledesma, Miguel A.; Grassi, Stefano (2017-08-01)
DSGE models are typically estimated assuming the existence of certain primal shocks that drive macroeconomic fluctuations. We analyze the consequences of estimating shocks that are "non-existent" and propose a method to select the primal shocks driving macroeconomic uncertainty. Forcing these non-existing shocks in estimation produces a downward bias in the estimated internal persistence of the model. We show how these distortions can be reduced by using priors for standard deviations whose support includes zero. The method allows us to accurately select primal shocks and estimate model ...
Working Paper Series , Paper WP-2017-20

Working Paper
Delphic and Odyssean Monetary Policy Shocks: Evidence from the Euro Area
Ferroni, Filippo; Andrade, Philippe (2018-07-26)
We use financial intraday data to identify monetary policy surprises in the euro area. We find that monetary policy statements and press conferences after European Central Bank (ECB) Governing Council meetings convey information that moves the yield curve far out. Moreover, the nature of the information revealed in a narrow window around these statements and press conferences evolved over time. Until 2013, unexpected variations in future interest rates were positively correlated with the changes in market-based measure of inflation expectations consistent with news on future macroeconomic ...
Working Paper Series , Paper WP-2018-12

Working Paper
Selection Without Exclusion
Hu, Luojia; Honore, Bo E. (2018-07-02)
It is well understood that classical sample selection models are not semiparametrically identified without exclusion restrictions. Lee (2009) developed bounds for the parameters in a model that nests the semiparametric sample selection model. These bounds can be wide. In this paper, we investigate bounds that impose the full structure of a sample selection model with errors that are independent of the explanatory variables but have unknown distribution. We find that the additional structure in the classical sample selection model can significantly reduce the identified set for the parameters ...
Working Paper Series , Paper WP-2018-10

Report
The behavior of uncertainty and disagreement and their roles in economic prediction: a panel analysis
Tracy, Joseph; Rich, Robert W. (2017-02-01)
This paper examines point and density forecasts from the European Central Bank?s Survey of Professional Forecasters. We derive individual uncertainty measures along with individual point- and density-based measures of disagreement. We also explore the relationship between uncertainty and disagreement, as well as their roles in respondents? forecast performance and forecast revisions. We observe substantial heterogeneity in respondents? uncertainty and disagreement. In addition, there is little co-movement between uncertainty and disagreement, and forecast performance shows a more robust ...
Staff Reports , Paper 808

Working Paper
A Note on the Finite Sample Bias in Time Series Cross-Validation
Lusompa, Amaze (2025-11-24)
It is well known that model selection via cross validation can be biased for time series models. However, many researchers have argued that this bias does not apply when using cross-validation with vector autoregressions (VAR) or with time series models whose errors follow a martingale-like structure. I show that even under these circumstances, performing cross-validation on time series data will still generate bias in general.
Research Working Paper , Paper RWP 25-17

Working Paper
Liquidity Networks, Interconnectedness, and Interbank Information Asymmetry
Harris, Jeffrey H.; Brunetti, Celso; Mankad, Shawn (2021-03-19)
Network analysis has demonstrated that interconnectedness among market participants results in spillovers, amplifies or absorbs shocks, and creates other nonlinear effects that ultimately affect market health. In this paper, we propose a new directed network construct, the liquidity network, to capture the urgency to trade by connecting the initiating party in a trade to the passive party. Alongside the conventional trading network connecting sellers to buyers, we show both network types complement each other: Liquidity networks reveal valuable information, particularly when information ...
Finance and Economics Discussion Series , Paper 2021-017

Working Paper
Understanding Models and Model Bias with Gaussian Processes
Palmer, Nathan M.; Cook, Thomas R. (2023-06-15)
Despite growing interest in the use of complex models, such as machine learning (ML) models, for credit underwriting, ML models are difficult to interpret, and it is possible for them to learn relationships that yield de facto discrimination. How can we understand the behavior and potential biases of these models, especially if our access to the underlying model is limited? We argue that counterfactual reasoning is ideal for interpreting model behavior, and that Gaussian processes (GP) can provide approximate counterfactual reasoning while also incorporating uncertainty in the underlying ...
Research Working Paper , Paper RWP 23-07

Working Paper
A Composite Likelihood Approach for Dynamic Structural Models
Matthes, Christian; Canova, Fabio (2018-07-23)
We describe how to use the composite likelihood to ameliorate estimation, computational, and inferential problems in dynamic stochastic general equilibrium models. We present a number of situations where the methodology has the potential to resolve well-known problems. In each case we consider, we provide an example to illustrate how the approach works and its properties in practice.
Working Paper , Paper 18-12

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